Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Traditional cellular network requires that a user equipment(UE) should associate to the same base station(BS) in both the downlink(DL) and the uplink(UL). Based on dual connectivity(DC) introduced in LTE-Advanced R12,...Traditional cellular network requires that a user equipment(UE) should associate to the same base station(BS) in both the downlink(DL) and the uplink(UL). Based on dual connectivity(DC) introduced in LTE-Advanced R12, DL/UL decouple access scheme has been proposed, which is especially suitable for heterogeneous networks(Het Nets). This paper is the pioneer to take the DL/UL decouple access scheme into consideration and develop a novel resource allocation algorithm in a two-tier Het Net to improve the total system throughput in the UL and ease the load imbalance between macro base stations(MBSs) and pico base stations(PBSs). A model is formulated as a nonlinear integer programming, and the proposed algorithm is a sub-optimal algorithm based on the graph theory. First, an undirected and weighted interference graph is obtained. Next, the users are grouped to let users with large mutual interferences to be assigned to different clusters. Then, the users in different clusters are allocated to different resource blocks(RBs) by using the Hungarian algorithm. Simulation results show that the proposed algorithm can provide great promotions for both the total system throughput and the average cell edge user throughput and successfully ease the load imbalance between MBSs and PBSs.展开更多
In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectr...In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectrum aggregation are not optimal or suitable for CR based heterogeneous networks(Het Nets). Consequently, the authors propose a novel resource scheduling scheme for spectrum aggregation in CR based Het Nets, termed as cognitive radio based resource scheduling(CR-RS) scheme. CR-RS has a three-level structure. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users(PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-RS. The simulation results show that CR-RS has the better performance than the current resource scheduling schemes in the CR based Het Nets. Meanwhile, CR-RS is also effective in other spectrum aggregation systems which are not CR based HetNets.展开更多
In this study,an explicit adaptive traffic allocation scheme for Machine-to-Machine(M2M)service is proposed to achieve optimum distribution in heterogeneous networks.Based on the characteristics of M2M services,the pr...In this study,an explicit adaptive traffic allocation scheme for Machine-to-Machine(M2M)service is proposed to achieve optimum distribution in heterogeneous networks.Based on the characteristics of M2M services,the presented scheme is formulated as a convex optimization problem that maximises the utility of the M2M service,and then determines how to allocate the total rate among the multiple access networks.The analysis and numerical simulations indicate that the proposed scheme makes a significant improvement in performance compared with the traditional schemes.展开更多
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the National Natural Science Foundation General Program of China under Grant No.61171110the National Basic Research Program of China under Grant No.2013CB329003
文摘Traditional cellular network requires that a user equipment(UE) should associate to the same base station(BS) in both the downlink(DL) and the uplink(UL). Based on dual connectivity(DC) introduced in LTE-Advanced R12, DL/UL decouple access scheme has been proposed, which is especially suitable for heterogeneous networks(Het Nets). This paper is the pioneer to take the DL/UL decouple access scheme into consideration and develop a novel resource allocation algorithm in a two-tier Het Net to improve the total system throughput in the UL and ease the load imbalance between macro base stations(MBSs) and pico base stations(PBSs). A model is formulated as a nonlinear integer programming, and the proposed algorithm is a sub-optimal algorithm based on the graph theory. First, an undirected and weighted interference graph is obtained. Next, the users are grouped to let users with large mutual interferences to be assigned to different clusters. Then, the users in different clusters are allocated to different resource blocks(RBs) by using the Hungarian algorithm. Simulation results show that the proposed algorithm can provide great promotions for both the total system throughput and the average cell edge user throughput and successfully ease the load imbalance between MBSs and PBSs.
基金supported by Major National Science and Technology Project(2014ZX03004003-005)Municipal Exceptional Academic Leaders Foundation (2014RFXXJ002)China Postdoctoral Science Foundation (2014M561347)
文摘In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectrum aggregation are not optimal or suitable for CR based heterogeneous networks(Het Nets). Consequently, the authors propose a novel resource scheduling scheme for spectrum aggregation in CR based Het Nets, termed as cognitive radio based resource scheduling(CR-RS) scheme. CR-RS has a three-level structure. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users(PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-RS. The simulation results show that CR-RS has the better performance than the current resource scheduling schemes in the CR based Het Nets. Meanwhile, CR-RS is also effective in other spectrum aggregation systems which are not CR based HetNets.
基金supported by the National Natural Science Foundation of Chinaunder Grant No.60971125the National Science and Technology Major Project of the Ministry of Science and Technology of Chinaunder Grant No.2012ZX03005-010the China Scholarship Council
文摘In this study,an explicit adaptive traffic allocation scheme for Machine-to-Machine(M2M)service is proposed to achieve optimum distribution in heterogeneous networks.Based on the characteristics of M2M services,the presented scheme is formulated as a convex optimization problem that maximises the utility of the M2M service,and then determines how to allocate the total rate among the multiple access networks.The analysis and numerical simulations indicate that the proposed scheme makes a significant improvement in performance compared with the traditional schemes.
